Zhen Li

Bio: Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.

Enzyme-Responsive Bioprobes Based on the Mechanism of Aggregation-Induced Emission

Abstract: Enzymes play an indispensable role in maintaining normal life activities. The abnormalities of content and activity in specific enzymes are usually associated with the occurrence and the developmen...

High-Performance Formamidinium-Based Perovskite Solar Cells via Microstructure-Mediated δ-to-α Phase Transformation

Abstract: The δ → α phase transformation is a crucial step in the solution-growth process of formamidinium-based lead triiodide (FAPbI3) hybrid organic–inorganic perovskite (HOIP) thin films for perovskite solar cells (PSCs). Because the addition of cesium (Cs) stabilizes the α phase of FAPbI3-based HOIPs, here our research focuses on FAPbI3(Cs) thin films. We show that having a large grain size in the δ-FAPbI3(Cs) non-perovskite intermediate films is essential for the growth of high-quality α-FAPbI3(Cs) HOIP thin films. Here grain coarsening and phase transformation occur simultaneously during the thermal annealing step. A large starting grain size in the δ-FAPbI3(Cs) thin films suppresses grain coarsening, precluding the formation of voids at the final α-FAPbI3(Cs)–substrate interfaces. PSCs based on the interface void-free α-FAPbI3(Cs) HOIP thin films are much more efficient and stable in the ambient atmosphere. This interesting finding inspired us to develop a simple room-temperature aging method for preparing ...

Portable Upconversion Nanoparticles-Based Paper Device for Field Testing of Drug Abuse.

Abstract: We report the first portable upconversion nanoparticles (UCNPs)-based paper device for road-side field testing of cocaine. Upon the recognition of cocaine by two pieces of rationally designed aptamer fragments, the luminescence of UCNPs immobilized on the paper is quenched by Au nanoparticles (AuNPs), which indicates the cocaine concentration. This device can give quantitative results in a short time with high sensitivity using only a smartphone as the apparatus. Moreover, this device is applicable in human saliva samples, and it also can be used to monitor the cocaine content change in blood samples. The results of this work demonstrate the prospect of developing UCNPs-based paper devices for field testing of drug abuse.

Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multi-Center Study During the COVID-19 Outbreak

Abstract: The novel COVID-19 outbreak has affected more than 200 countries and territories as of March 2020. Given that patients with cancer are generally more vulnerable to infections, systematic analysis of diverse cohorts of patients with cancer affected by COVID-19 is needed. We performed a multicenter study including 105 patients with cancer and 536 age-matched noncancer patients confi rmed with COVID-19. Our results showed COVID-19 patients with cancer had higher risks in all severe outcomes. Patients with hematologic cancer, lung cancer, or with metastatic cancer (stage IV) had the highest frequency of severe events. Patients with nonmetastatic cancer experienced similar frequencies of severe conditions to those observed in patients without cancer. Patients who received surgery had higher risks of having severe events, whereas patients who underwent only radiotherapy did not demonstrate signifi cant differences in severe events when compared with patients without cancer. These fi ndings indicate that patients with cancer appear more vulnerable to SARS-CoV-2 outbreak. SIGnIFICAnCE: Because this is the fi rst large cohort study on this topic, our report will provide muchneeded information that will benefi t patients with cancer globally. As such, we believe it is extremely important that our study be disseminated widely to alert clinicians and patients. 1 Department of Gynecological Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China. 2 Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, China. 3 Hubei Cancer Clinical Study Center, Wuhan, China. 4 Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts. 5 Department of Pediatrics, Harvard Medical School, Boston, Massachusetts. 6 Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. 7 Department of Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China. 8 Cancer Center, Union Hospital affi liated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China. 9 Department of Emergency, The Central Hospital of Wuhan affi liated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China. 10 Department of Gynecology, The Central Hospital of Huanggang, Huanggang, Hubei, China 11 Affi liated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, Guangdong Province, China. 12 Department of Ultrasound, Zhongnan Hospital of Wuhan University, Wuhan, China. 13 Department of Infectious Disease, Zhongnan Hospital of Wuhan University, Wuhan, China. 14 Cancer Center, Tongji Hospital affi liated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China. 15 Department of Oncology, The Central Hospital of Wuhan affi liated to Tongji Medical College of Huazhong University of Science and Technology, Wuhan, Hubei, China. 16 Department of Radiology, Hubei Cancer Hospital, Wuhan, Hubei, China. 17 Department of Thoracic Surgery, Hubei Cancer Hospital, Wuhan, Hubei, China. 18 Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China. 19 Department of Oncology, Wuhan Puren Hospital, Wuhan, Hubei, China. 20 Department of Obstetrics and Gynecology, The Central Hospital of Xianning, Xianning, Hubei, China. 21 Department of Oncology, The Central Hospital of Xiaogan, Xiaogan, China. 22 Department of Obstetrics and Gynecology, The People's Hospital of Huangmei, Huangmei, Hubei, China. 23 Department of Obstetrics and Gynecology, Xiangyang First People's Hospital affi liated to Hubei University of Medicine, Xiangyang, Hubei, China. 24 Department of Obstetrics and Gynecology, The People's Hospital of Shiyan, Shiyan, Hubei, China. 25 Harvard Stem Cell Institute, Harvard Medical School, Boston, Massachusetts. 26 Cancer Science Institute of Singapore, National University of Singapore, Singapore. 27 Harvard T.H. Chan School of Public Health, Dana-Farber, Harvard Cancer Center, Boston, Massachusetts. note: Supplementary data for this article are available at Cancer Discovery Online (http://cancerdiscovery.aacrjournals.org/). Cancer Research. on January 10, 2021. © 2020 American Association for cancerdiscovery.aacrjournals.org Downloaded from Published OnlineFirst April 28, 2020; DOI: 10.1158/2159-8290.CD-20-0422

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Highly accurate protein structure prediction with AlphaFold

Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.